In a generic object recognition technology, feature value of an article is extracted from an image captured by an image capturing element, and is compared with feature value of each reference article registered in a dictionary beforehand. A category of the article is recognized based on a similarity degree therebetween. Moreover, a technology in which commodities such as vegetables and fruits and the varieties thereof are identified with the generic object recognition technology described above to execute sales registration of the commodity based on identification results has been proposed.
In a dictionary used in the above generic object recognition, feature value for collation (comparison) is registered in advance. However, articles such as vegetables and fruits which are recognition targets of the generic object recognition are different in texture and color of a surface thereof depending on producing areas, even for the same variety in some cases. If the texture and the color of the surface are different even for the same variety, the similarity degree by comparison with the feature value registered in the dictionary becomes small. Thus, there is a problem that a recognition rate is lowered. Therefore, according to the conventional technology, a learning function is provided to newly register or update the feature value from images that are newly captured.
However, if a foreign object such as a hand of an operator is contained in a captured image for learning, there is a possibility that the recognition rate is lowered compared with the recognition rate before the learning operation is carried out. Thus, it is desirable to exclude the captured image containing the foreign object from a target of the learning operation. However, in the conventional technology, the operator needs to confirm states of the captured images one by one to determine whether or not the captured image is set as the target of the learning operation and thus it is very troublesome and an inefficient operation for the operator.